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Gait Authentication based on Spiking Neural Networks

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dc.contributor.authorRúa, Enrique Argones
dc.contributor.authorvan Hamme, Tim
dc.contributor.authorPreuveneers, Davy
dc.contributor.authorJoosen, Wouter
dc.contributor.editorBrömme, Arslan
dc.contributor.editorBusch, Christoph
dc.contributor.editorDamer, Naser
dc.contributor.editorDantcheva, Antitza
dc.contributor.editorGomez-Barrero, Marta
dc.contributor.editorRaja, Kiran
dc.contributor.editorRathgeb, Christian
dc.contributor.editorSequeira, Ana
dc.contributor.editorUhl, Andreas
dc.date.accessioned2021-10-04T08:43:52Z
dc.date.available2021-10-04T08:43:52Z
dc.date.issued2021
dc.description.abstractIn this paper we address gait authentication using a novel approach based on spiking neural networks (SNNs). This technology has proven advantages regarding energy consumption and it is a perfect match with some proposed neuromorphic hardware chips, which can lead to a broader adoption of user device applications of artificial intelligence technologies. One of the challenges when using this technology is the training of the network itself, since it is not straightforward to apply well-known error backpropagation, massively used in traditional artificial neural networks (ANNs). In this paper we propose a new derivation of error backpropagation for the spiking neural networks that integrates lateral inhibition and provides competitive results when compared to state of the art ANNs in the context of IMU-based gait authentication.en
dc.identifier.isbn978-3-88579-709-8
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/37472
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-315
dc.subjectSpiking neural networks
dc.subjectContinuous authentication
dc.subjectOpen set biometric authentication
dc.subjectIMU gait authentication
dc.titleGait Authentication based on Spiking Neural Networksen
dc.typeText/Conference Paper
gi.citation.endPage60
gi.citation.publisherPlaceBonn
gi.citation.startPage51
gi.conference.date15.-17. September 2021
gi.conference.locationInternational Digital Conference
gi.conference.sessiontitleRegular Research Papers

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P315 - BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group